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"""TensorPEFT: tensor gradient-pursuit adapters.
Matrices of equal shape across all transformer blocks are stacked into one update tensor
(layer x [role] x d_in x d_out). Each group's update is a sum of rank-one CP atoms
dW = sum_i a_i u_i(layer) o u_i(role) o u_i(in) o u_i(out).
``init_atoms`` runs one gradient probe, and the atoms are the leading rank-one CP components of
the negative gradient tensor (HOPM with deflation). Coefficients start at zero, so the model is
unchanged until training starts; all coefficients and factors are then trained normally.
"""
import functools
import os
import torch
import torch.nn as nn
from . import __version__
from .config import CONFIG_NAME, WEIGHTS_NAME, TensorPEFTConfig, _resolve
from .cp import cp_value, outer, rank1_cp
from .layers import attach_hooks, find_slots
class TensorGroup(nn.Module):
def __init__(self, slots, k, alpha_scale):
super().__init__()
self.slots = slots
self.d_in, self.d_out = slots[0].shape
self.layers = sorted({s.layer for s in slots})
self.roles = sorted({s.role for s in slots})
self.has_p = len(self.roles) > 1
self.shape = (len(self.layers),) + ((len(self.roles),) if self.has_p else ()) + (self.d_in, self.d_out)
self.index = {id(s): (self.layers.index(s.layer),) + ((self.roles.index(s.role),) if self.has_p else ())
for s in slots}
self.k = k
self.alpha_scale = alpha_scale
dev = slots[0].module.weight.device
self.alpha_raw = nn.Parameter(torch.zeros(k, device=dev))
self.factors = nn.ParameterList([nn.Parameter(torch.zeros(k, n, device=dev)) for n in self.shape])
self.register_buffer("initialized", torch.tensor(False, device=dev))
self._cache = None
@property
def name(self):
return f"{'+'.join(self.roles)} {list(self.shape)}"
def n_params(self):
return self.k * (1 + sum(self.shape))
def meta(self):
return {"roles": self.roles, "layers": self.layers, "shape": list(self.shape)}
def prepare(self):
if not bool(self.initialized):
self._cache = None
return
a = self.alpha_raw * self.alpha_scale
F = list(self.factors)
C = torch.einsum("k,kl,kp->klp", a, F[0], F[1]) if self.has_p else a[:, None] * F[0]
self._cache = (C, F[-2], F[-1])
def delta(self, slot, x):
if self._cache is None:
return None
C, U_in, U_out = self._cache
c = C[(slice(None),) + self.index[id(slot)]]
return ((x.to(U_in.dtype) @ U_in.T) * c) @ U_out
@torch.no_grad()
def dense_delta(self, slot):
self.prepare()
if self._cache is None:
return None
C, U_in, U_out = self._cache
c = C[(slice(None),) + self.index[id(slot)]]
return (U_in.T * c) @ U_out
@torch.no_grad()
def stacked_grad(self):
w = self.slots[0].module.weight
G = torch.zeros(self.shape, dtype=torch.float32, device=w.device)
for s in self.slots:
G[self.index[id(s)]] = s.grad().float()
return G
class TensorPEFTModel(nn.Module):
"""Wraps a Hugging Face causal LM. Use :func:`get_tensorpeft_model` to create one."""
def __init__(self, base_model, config: TensorPEFTConfig):
super().__init__()
self.base_model = base_model
self.peft_config = config
config.tensorpeft_version = __version__
if config.base_model_name_or_path is None:
config.base_model_name_or_path = getattr(getattr(base_model, "config", None), "_name_or_path", None)
for p in base_model.parameters():
p.requires_grad_(False)
self.slots = find_slots(base_model, config.target_modules, config.exclude_modules, config.split_fused_qkv)
by_shape = {}
for s in self.slots:
by_shape.setdefault(s.shape, []).append(s)
self.groups = nn.ModuleList([TensorGroup(ss, config.atoms_per_group, config.alpha_scale)
for ss in by_shape.values()])
self._group_of = {id(s): g for g in self.groups for s in g.slots}
self._handles = attach_hooks(self.slots, lambda s, x: self._group_of[id(s)].delta(s, x))
self._handles.append(base_model.register_forward_pre_hook(lambda m, a: self._prepare()))
self._modules_w = list({id(s.module): s.module for s in self.slots}.values())
# forward with the base model's signature (so HF Trainer keeps the right dataset columns)
def forward(*args, **kwargs):
return self.base_model(*args, **kwargs)
functools.update_wrapper(forward, base_model.forward)
self.forward = forward
# ------------------------------------------------------------------ plumbing
def _prepare(self):
for g in self.groups:
g.prepare()
def __getattr__(self, name):
try:
return super().__getattr__(name)
except AttributeError:
return getattr(super().__getattr__("base_model"), name)
def get_base_model(self):
return self.base_model
@property
def is_initialized(self):
return all(bool(g.initialized) for g in self.groups)
def get_nb_trainable_parameters(self):
trainable = sum(p.numel() for p in self.parameters() if p.requires_grad)
total = sum(p.numel() for p in self.base_model.parameters()) + trainable
return trainable, total
def print_trainable_parameters(self):
t, a = self.get_nb_trainable_parameters()
print(f"trainable params: {t:,} || all params: {a:,} || trainable%: {100 * t / a:.4f}")
def describe(self):
return [f"{g.name}: {g.k} atoms, {g.n_params():,} params" for g in self.groups]
# ------------------------------------------------------------------ initialisation
def init_atoms(self, batches, n_batches=None, loss_fn=None, verbose=True):
"""Choose all atoms from one gradient probe.
batches: iterable of dicts (``input_ids``, optional ``attention_mask`` / ``labels``) or of
input-id tensors (causal-LM labels = inputs). A DataLoader works.
n_batches: how many batches to use (default ``config.init_batches``).
loss_fn: optional ``loss_fn(base_model, batch) -> scalar``.
Returns, per group, each atom's initial descent rate <-G, atom> (= -dL/da at a = 0).
"""
n_batches = n_batches or self.peft_config.init_batches
dev = self._modules_w[0].weight.device
was_training = self.base_model.training
self.base_model.eval()
for m in self._modules_w:
m.weight.requires_grad_(True)
m.weight.grad = None
used = 0
for b in batches:
if used >= n_batches:
break
if loss_fn is not None:
loss = loss_fn(self.base_model, b)
else:
if torch.is_tensor(b):
b = {"input_ids": b}
b = {k: (v.to(dev) if torch.is_tensor(v) else v) for k, v in b.items()}
if "labels" not in b:
b["labels"] = b["input_ids"]
loss = self.base_model(**b).loss
loss.backward()
used += 1
if used == 0:
raise ValueError("init_atoms: no batches given")
report = []
with torch.no_grad():
for g in self.groups:
negG = -g.stacked_grad() / used # mean gradient over the batches used
T = negG.clone()
sig = []
for i in range(g.k):
us, s = rank1_cp(T, iters=self.peft_config.hopm_iters)
for pl, u in zip(g.factors, us):
pl[i].copy_(u)
T = T - s * outer(us) # deflation
sig.append(float(cp_value(negG, us)))
del negG, T
g.alpha_raw.zero_()
g.initialized.fill_(True)
report.append(sig)
for m in self._modules_w:
m.weight.grad = None
m.weight.requires_grad_(False)
for p in self.parameters():
p.grad = None
self.base_model.train(was_training)
if verbose:
for g, s in zip(self.groups, report):
print(f"TensorPEFT {g.name}: {g.k} atoms, initial descent rate {s[0]:.4g} .. {s[-1]:.4g}")
return report
# ------------------------------------------------------------------ save / load
def adapter_state_dict(self):
sd = {}
for gi, g in enumerate(self.groups):
sd[f"group{gi}.alpha_raw"] = g.alpha_raw.detach().cpu().contiguous()
for m, pl in enumerate(g.factors):
sd[f"group{gi}.factor{m}"] = pl.detach().cpu().contiguous()
return sd
def save_pretrained(self, save_directory, **kwargs):
if not self.is_initialized:
raise RuntimeError("call init_atoms(...) before saving")
from safetensors.torch import save_file
os.makedirs(save_directory, exist_ok=True)
self.peft_config.save_pretrained(save_directory, extra={"groups": [g.meta() for g in self.groups]})
save_file(self.adapter_state_dict(), os.path.join(save_directory, WEIGHTS_NAME),
metadata={"format": "pt", "library": "tensorpeft"})
readme = os.path.join(save_directory, "README.md")
if not os.path.exists(readme):
with open(readme, "w") as f:
f.write(_model_card(self))
@classmethod
def from_pretrained(cls, base_model, model_id, is_trainable=False, **hub_kwargs):
config, raw = TensorPEFTConfig.from_pretrained(model_id, **hub_kwargs)
model = cls(base_model, config)
saved = raw.get("groups")
if saved is not None:
mine = [g.meta() for g in model.groups]
if [(m["roles"], m["shape"]) for m in mine] != [(m["roles"], m["shape"]) for m in saved]:
raise ValueError("adapter does not match this base model's architecture")
from safetensors.torch import load_file
sd = load_file(_resolve(model_id, WEIGHTS_NAME, **hub_kwargs))
with torch.no_grad():
for gi, g in enumerate(model.groups):
g.alpha_raw.copy_(sd[f"group{gi}.alpha_raw"])
for m, pl in enumerate(g.factors):
pl.copy_(sd[f"group{gi}.factor{m}"])
g.initialized.fill_(True)
for p in model.parameters():
if p.requires_grad and not is_trainable:
p.requires_grad_(False)
return model
def push_to_hub(self, repo_id, private=None, token=None, commit_message="Upload TensorPEFT adapter"):
import tempfile
from huggingface_hub import HfApi
api = HfApi(token=token)
api.create_repo(repo_id, private=private, exist_ok=True)
with tempfile.TemporaryDirectory() as d:
self.save_pretrained(d)
return api.upload_folder(repo_id=repo_id, folder_path=d, commit_message=commit_message)
# ------------------------------------------------------------------ merge
@torch.no_grad()
def merge_and_unload(self):
"""Write dW into the base weights and return the plain base model (no inference overhead)."""
for s in self.slots:
d = self._group_of[id(s)].dense_delta(s)
if d is not None:
w = s.weight()
w.add_(d.to(w.dtype))
return self.unload()
def unload(self):
"""Remove the adapter hooks without merging and return the base model."""
for h in self._handles:
h.remove()
self._handles = []
return self.base_model
def get_tensorpeft_model(model, config: TensorPEFTConfig = None):
"""Wrap a Hugging Face model with TensorPEFT. Call ``.init_atoms(batches)`` before training."""
return TensorPEFTModel(model, config or TensorPEFTConfig())
def _model_card(m):
c = m.peft_config
t, _ = m.get_nb_trainable_parameters()
return f"""---
library_name: tensorpeft
base_model: {c.base_model_name_or_path or 'unknown'}
tags:
- tensorpeft
- peft
---
# TensorPEFT adapter
Base model: `{c.base_model_name_or_path}`; {c.atoms_per_group} atoms per tensor group; {t:,} adapter parameters.
```python
from transformers import AutoModelForCausalLM
from tensorpeft import TensorPEFTModel
base = AutoModelForCausalLM.from_pretrained("{c.base_model_name_or_path}")
model = TensorPEFTModel.from_pretrained(base, "<this repo id>")
model = model.merge_and_unload() # optional
```
"""
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